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2 Capacity Planning Scenarios Service provider needs to migrate his media site to a new infrastructure. While he has information about the site workload (the media the server logs reflecting the accesses to the media site in the past), it is a problem to map workload requirements in the resource requirements Can we design a tool helping to accomplish the capacity planning tasks? The goal of the proposed capacity planning tool is to provide the best cost/performance configuration for support of a known media service workload.

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4 Main Components Two main components: –A media workload profiler MediaProf that extracts a set of quantitative and qualitative parameters characterizing the service demand –The capacity measurements of h/w and s/w solutions using a specially designed set of media benchmarks; The capacity planning tool matches the requirements of the media service workload profile, SLAs and configuration constraints to produce the best available cost/performance solution.

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6 Workload-Aware Performance Model of Streaming Media Server Capacity How to compute the expected media server capacity for realistic workload if the measured capacities under the basic benchmarks are given. We introduce cost function which defines a fraction of system resources needed to support a particular stream depending on –file encoding bit rate and –file access type (streamed from memory or disk). Introduced cost function uses a single value to reflect the combined resource requirements such as CPU, disk, memory and server bandwidth necessary to support a particular media request.

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9 Segment-based Memory Model To stream the file from memory, it is not necessary to have the whole file in memory!

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10 Media Workload Characterization Example: analysis of the HP Corporate Media Site over a period of 1 year duration: Number of concurrent connections Peak Bandwidth requirements Number of requests served from memoryNumber of requests served from disk

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11 Overall Capacity Planning Process There are several logical steps in the capacity planning procedure: –Computing the media site workload profiles for different memory sizes of interest. During the initial step, we assume a single node cluster: N=1 –Computing the service demand profile. The service demand profile is the ordered list of pairs: (time duration, service demand). For example: (300, 4.5 ) (600, 4 ) (2000, 3.8) (1000, 3.5) …

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12 Overall Capacity Planning Process (cont.) –Combining the service demand requirements, the SLAs, and the configuration constraints: SLAs: Based on the past workload history, find the configuration that 99% of the time is capable of processing the load; Constraints: Based on the past workload history, find the configuration that 90% of the time is utilized under 70% of its capacity.

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13 Overall Capacity Planning Additionally, we need to do a cluster sizing with an appropriate load balancing strategy

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14 Evaluating Load Balancing Solutions For an accurate cluster sizing we need to take into account both: increased processing power (N nodes) and increased memory size (N times M) In our capacity planning tool, we implemented 2 strategies: –Round Robin –LARD (locality-aware ): first access goes to random node in the cluster, but subsequest requests to the same file are send to the same node. If the outcome of the first iteration is k nodes then –Partition original workload in k sub-trace W 1, W 2, …, W k where Dispatcher employs the corresponding load balancing strategy; –Compute media workload profile for each W 1, W 2, …, W k using MediaProf –Merging the computed sub-workload profiles and computing overall service demand profile

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15 Performance Results For workload generation, we used MediSyn: publicly available synthetic workload generator. W SYN (with parameters that are typical for enterprise media workloads) 20% of videos are 0-2 min long 10% of videos are 2-5 min long 13% of videos are 5-10 min long Video Duration 23% of videos are 10-30 min long 21% of videos are 30-60 min long 13% of videos are longer than 60 min 5% of videos encoded at 56 Kb/s 20% of videos encoded at 112Kb/s 50% of videos encoded at 256Kb/s File Encoding Bit Rate 25% of videos encoded at 500Kb/s

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20 Cluster sizing Cluster sizing results for a given synthetic workload are summarized in the following Table: Locality aware load balancing strategy utilizes the increased cluster memory more efficiently, and requires less nodes to Support the same traffic.

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21 Conclusion We proposed a new unified benchmarking and capacity planning framework: –Measure media server via a set of basic benchmarks; –Derives the resource requirements using a single value cost function; –Estimate the service capacity requirements from the proposed media workload profile. In future work, we would like to incorporate the availability requirements into the proposed capacity planning framework.